SFAG-Net: A Retinal Vessel Segmentation Network Based on Synergistic Fusion and Adaptive Guidance

SFAG-Net: A Retinal Vessel Segmentation Network Based on Synergistic Fusion and Adaptive Guidance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article SFAG-Net: A Retinal Vessel Segmentation Network Based on Synergistic Fusion and Adaptive Guidance Gengtao Sun, Lijie Xie, Xiwang Xie, Hao Guo, Liuliang Yong This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8848681/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Automatic segmentation of retinal vessels in fundus images provides a crucial basis for the clinical diagnosis of ocular pathologies. However, fundus image analysis presents significant challenges due to the complex vascular topology, low background contrast, and substantial noise interference. Convolutional Neural Networks possess powerful inductive biases, whereas the Transformer excels at capturing long-range dependencies. Leveraging these attributes, this paper proposes a retinal vessel segmentation network based on synergistic fusion and adaptive guidance. The network employs a dual-branch encoder to extract local detail features and global semantic features, respectively. To enable effective complementarity between the two branches, a Synergy Fusion Integrator is constructed using the cross-attention mechanism. Additionally, the Adaptive Feature Guidance Module is designed to enhance multi-scale detail perception. This module dynamically reinforces critical features via a spatial attention mechanism, significantly improving the recognition accuracy of thin vessels and boundary structures. Experimental results demonstrate that the proposed network outperforms current advanced vessel segmentation methods on three public retinal vessel segmentation datasets. It exhibits robust segmentation performance and strong generalization capability, offering reliable technical support for computer-aided clinical diagnosis. Retinal vessel segmentation Attention mechanism Synergy Fusion Integrator Adaptive Feature Guidance Module Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviews received at journal 27 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers agreed at journal 24 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 13 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 11 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8848681","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596515293,"identity":"44f2d5cd-3d7c-4bb6-9300-7e7bed9114f7","order_by":0,"name":"Gengtao Sun","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Gengtao","middleName":"","lastName":"Sun","suffix":""},{"id":596515300,"identity":"57cd8b94-028a-47c5-9301-4fdc4c72b422","order_by":1,"name":"Lijie Xie","email":"","orcid":"","institution":"Huanghe University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Lijie","middleName":"","lastName":"Xie","suffix":""},{"id":596515302,"identity":"9d93b2af-7245-4564-a3d1-8bb142c2afbe","order_by":2,"name":"Xiwang Xie","email":"","orcid":"","institution":"Henan University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Xiwang","middleName":"","lastName":"Xie","suffix":""},{"id":596515303,"identity":"07a75b37-a844-45f8-8e28-3ec5bc493874","order_by":3,"name":"Hao Guo","email":"","orcid":"","institution":"Dalian Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Guo","suffix":""},{"id":596515305,"identity":"88863b42-f086-4c8c-9c8d-ec13549e5688","order_by":4,"name":"Liuliang Yong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYFAC5sYDIIpfAsyTkCFCC2MDWIvkDCALqIWHeC0GN8BaGAhr4Z/d2HCYp+KO3ebbzccf3aix4GFgP3x0Az4tEncOArWceZa87c6xxOacY0CH8aSl3cCnxUAiseEwb9vhZLMbOYbNOWxALRI8ZsRpMZ4B0vKPBC12BhJALbltRGiRuJHYcHDOmcMJEjfSEmfn9knwsBHyC/+M5IMP3lQctgcyDnzO+VYnx89++BheLSDABIyLxAYYj42QchBg/MHAYE+MwlEwCkbBKBihAAASJk3VqlGxgwAAAABJRU5ErkJggg==","orcid":"","institution":"Henan University of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Liuliang","middleName":"","lastName":"Yong","suffix":""}],"badges":[],"createdAt":"2026-02-11 07:54:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8848681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8848681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104398444,"identity":"ab81bdbf-d6dc-47a7-974e-29472d9b24c5","added_by":"auto","created_at":"2026-03-11 12:02:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23854635,"visible":true,"origin":"","legend":"","description":"","filename":"SFAGNetARetinalVesselSegmentationNetworkBasedonSynergisticFusionandAdaptiveGuidance1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8848681/v1_covered_e0aff1b5-94ab-4dfe-a13b-3b14cbdc7e5a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"SFAG-Net: A Retinal Vessel Segmentation Network Based on Synergistic Fusion and Adaptive Guidance","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-king-saud-university-computer-and-information-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of King Saud University Computer and Information Sciences](https://link.springer.com/journal/44443)","snPcode":"44443","submissionUrl":"https://submission.springernature.com/new-submission/44443/3","title":"Journal of King Saud University Computer and Information Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Retinal vessel segmentation, Attention mechanism, Synergy Fusion Integrator, Adaptive Feature Guidance Module","lastPublishedDoi":"10.21203/rs.3.rs-8848681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8848681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAutomatic segmentation of retinal vessels in fundus images provides a crucial basis for the clinical diagnosis of ocular pathologies. 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